Adaptive singular value decomposition for bearing fault diagnosis under strong noise interference

Variational Mode Decomposition (VMD) is a signal decomposition algorithm with excellent denoising ability. However, the drawback that VMD is unable to determine the input parameters adaptively seriously affects the decomposition results. For this issue, an optimized VMD method based on modified scale-space representation (MSSR-VMD) is proposed. Firstly, MSSR is proposed to segment the fault signal spectrum, acquiring modes' number and the initial center frequency for each mode adaptively. Moreover, a pre-decomposition step is added to the original VMD, which selects a target mode from divided frequency bands. Finally, the penalty factor of the target mode is adjusted during the iterative update of the VMD to achieve accurate extraction for the fault features. MSSR-VMD and other adaptive decomposition algorithms are employed to handle the simulated and experimental signals separately. By comparing the analysis results, the method has certain superiority in rolling bearing fault feature extraction.

“…The larger x is, the smoother the discrete SSR (i.e. L(m, x)) obtained, and the expression of L(m, x) is shown in equation (10):…”

Section: Scale-space Representation (Ssr)mentioning

confidence: 99%

An adaptive spectrum segmentation-based optimized VMD method and its application in rolling bearing fault diagnosis

Meng¹,

Wang²,

Liu³

et al. 2022

“…Then, the matrices generated by embedding the original time-domain or sub-band signals can be input to SVD for fault diagnosis [27]. Cui et al [28] introduced the adaptive SVD approach, capable of accurately determining the optimal embedding dimension of a trajectory matrix and automatically selecting singular values (SVs) to reconstruct signals for envelope analysis. Moreover, by combining SVD with other methods, it can extract FCFs of rolling bearings by suppressing background noise [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Isolation and identification of rolling bearing compound faults based on adaptive periodized singular spectrum analysis and Rényi entropy

Li,

Yan,

Hou

et al. 2024

Due to the coupling of multiple fault feature information and contamination of heavy background noise, it is a challenging task to accurately identify Rolling Bearing Compound Fault (RBCF). A method for isolating and identifying the RBCF is proposed by integrating the Adaptive Periodized Singular Spectrum Analysis (APSSA) with Rényi entropy (RE). The adaptive selection of embedding dimension of Hankel matrix in APSSA without setting parameters empirically is proposed, and a selection criterion for singular values is established to preprocess the vibration signals of rolling bearing and enhance fault periodic component. A RE-based threshold value is introduced to further isolate and decouple the impulse segments of vibration signal in the time domain. Considering inner raceway fault, outer raceway fault, ball fault and skidding, a comprehensive simulation model of compound fault is constructed by the response mechanism of different excited resources. Simulated and experimental data are applied to validate the effectiveness and practicability of proposed method. The results demonstrate that RBCF can be identified correctly by the proposed method under strong background noise.

“…Among these weak signal detection methods, SVD has exhibited a very good performance and is widely used to extract the weak features [10][11][12]. The singular value of the SVD reflects the intrinsic characteristics of the data and has good stability and invariance.…”

Section: Introductionmentioning

confidence: 99%

A new selection method of singular values using correlation coefficients for detecting weak exponential damped sinusoidal signals under strong noise

Zhang

2024

Weak signal detection has attracted considerable attention in a wide range of research fields, especially the weak signal detection under strong noise is an urgent problem researches are concerned with. In this paper, a new criterion to select singular values using correlation coefficient is proposed for weak exponential damped sinusoidal signal detection, which has a wide variety of signal processing applications. The innovation of our method lies in selecting K most informative singular values rather than the most energetic singular values. The proposed method measures the similarity between component signals and useful signal through autocorrelation function and correlation coefficient, which can preserve more information of the original signal and be more suitable for weak signal detection scenarios under strong noise. The numerical experiments and analysis are performed to verify the efficiency and effectiveness of our method, which indicates the proposed method is superior to the singular value selection methods based on energy or simple difference principle for correlation coefficients. Compared with stochastic resonance methods suitable for weak signal detection under strong background noise, our proposed method also has significant advantages. Thus, the proposed method is beneficial to theoretical analysis and engineering applications.